Speaker 1: Pushkin, you're listening to Brave New Planet, a podcast about
Speaker 1: amazing new technologies that could dramatically improve our world. Or
Speaker 1: if we don't make wise choices, could leave us a
Speaker 1: lot worse off. Utopia or dystopia. It's up to us
Speaker 1: pash the eye. On July sixteenth, nineteen sixty nine, Apollo
Speaker 1: eleven blasted off from the Kennedy Space Center near Cape Canaveral, Florida.
Speaker 1: Twenty five million Americans watched on television as the spacecraft
Speaker 1: ascended toward the heavens, carrying commander Neil Armstrong, Lunar Module
Speaker 1: pilot Buzz Aldron, and Command Module pilot Michael Collins their
Speaker 1: mission to be the first humans in history to set
Speaker 1: foot on the Moon. Four days later, on Sunday, July twentieth,
Speaker 1: the lunar module separated from the command ship and soon
Speaker 1: fired its rockets to begin its lunar descent. Five minutes later,
Speaker 1: disaster struck about a mile above the Moon's surface. Program
Speaker 1: alarms twelve O one and twelve O two sounded loudly,
Speaker 1: indicating that the mission computer was overloaded, and then, well,
Speaker 1: every American knows what happened next. Lost date of five
Speaker 1: good evening, my fellow Americans, President Richard Nick addressed a
Speaker 1: grieving nation. Fades has ordained that the men who went
Speaker 1: to the Moon to explore in peace will stay on
Speaker 1: the Moon to rest in peace. These brave men, Neil
Speaker 1: Armstrong and Edwin Auburn, know that there's no hope for
Speaker 1: their recovery, but they also know that there is hope
Speaker 1: for mankind in their sacrifice. He ended with the now
Speaker 1: famous words for every human being who looks up at
Speaker 1: the Moon and the nights to come, will know that
Speaker 1: there is some corner another word that is forever mankind.
Speaker 1: Wait a minute, that never happened. The Moon mission was
Speaker 1: a historic success. The three astronauts returned safely to ticker
Speaker 1: tape parades and a celebrity thirty eight day world tour.
Speaker 1: Those alarms actually did sound, but they turned out to
Speaker 1: be harmless. Nixon never delivered that speech. His speechwriter had
Speaker 1: written it, but it sat in a folder labeled an
Speaker 1: event of Moon disaster until now. The Nixon you just
Speaker 1: heard is a deep fake, part of a seven minute
Speaker 1: film created by artificial intelligence deep learning algorithms. The fake
Speaker 1: was made by the Center for Advanced Virtuality at the
Speaker 1: Massachusetts Institute of Technology as part of an art exhibit
Speaker 1: to raise awareness about the power of synthesized media. Not
Speaker 1: long ago, something like this would have taken a lot
Speaker 1: of time and money, But now it's getting easy. You
Speaker 1: can make new paintings in the style of French Impressionism,
Speaker 1: revived dead movie stars, help patients with nor degenerative disease,
Speaker 1: or soon maybe take a class on a tour of
Speaker 1: ancient rome. But as the technology quickly becomes democratized, we're
Speaker 1: getting to the point where almost anyone can create a
Speaker 1: fake video of a friend, an ex lover, a stranger,
Speaker 1: or a public figure that's embarrassing, pornographic, or perhaps capable
Speaker 1: of causing international chaos. Some argue that in a culture
Speaker 1: where fake news spreads like wildfire and political leaders deny
Speaker 1: the veracity of hard facts, deep fake media may do
Speaker 1: a lot more harm than good. Today's big question will
Speaker 1: synthesized media unleash a new wave of creativity or will
Speaker 1: it erode the already tenuous role of truth in our democracy?
Speaker 1: And is there anything we can do to keep it
Speaker 1: in check. My name is Eric Lander. I'm a scientist
Speaker 1: who works on ways to improve human health. I helped
Speaker 1: lead the Human Genome Project, and today I lead the
Speaker 1: Broad Institute of MIT and Harvard. In the twenty first century,
Speaker 1: powerful technologies have been appearing at a breathtaking pace related
Speaker 1: to the Internet, artificial intelligence, genetic engineering, and more. They
Speaker 1: have amazing potential upsides, but we can't ignore the risks
Speaker 1: that come with them. The decisions aren't just up to
Speaker 1: scientists or politicians. Whether we like it or not, we
Speaker 1: all of us are the stewards of a brave New Planet.
Speaker 1: This generation's choices will shape the future as never before.
Speaker 1: Coming up on today's episode of Brave New Planet, I
Speaker 1: speak with some of the leaders behind advances in synthesized media.
Speaker 1: You could, certainly, by the way, generate stories that could
Speaker 1: be fresh and interesting and new and personal for every child.
Speaker 1: We got emails from people who were quadruplegic and they
Speaker 1: asked us if we could make them dance. We hear
Speaker 1: from experts about some of the frightening ways that bad
Speaker 1: actors can use deep fakes. Creditors would chime in and say,
Speaker 1: you can absolutely make a deep fake sex video of
Speaker 1: your ex with thirty pictures. I've done it with twenty.
Speaker 1: Here's the things that keep me up at night right
Speaker 1: a video of Donald Trump saying I've launched nuclear weapons
Speaker 1: against Iran, and before anybody gets around to firing out
Speaker 1: whether this is real or not, we have global nuclear
Speaker 1: outdown and we explore how we might prevent the worst abuses.
Speaker 1: It's important that younger people advocate for the Internet that
Speaker 1: they want. We have to fight for it. We have
Speaker 1: to ask for different things. Stay with us, Chapter one,
Speaker 1: Abraham Lincoln's Head. To begin to understand and the significance
Speaker 1: of deep fake technology, I went to San Francisco to
Speaker 1: speak with a world expert on synthetic media. My name
Speaker 1: is Alexei or sometimes called Alyosha Afros, and I'm a
Speaker 1: professor at UC Berkeley and Computer Science and Lexical Engineering Department.
Speaker 1: My research is on computer vision, computer graphics, machine learning,
Speaker 1: various aspects of artificial intelligence. Where'd you grow up. I
Speaker 1: grew up in Saint Petersburg in Russia. I was one
Speaker 1: of those geeky kids playing around with computers or dreaming
Speaker 1: about computers. My first computer was actually the first Soviet
Speaker 1: personal computer. So you actually are involved in making sort
Speaker 1: of synthetic content, synthetic media, that's right. Alexei has invented
Speaker 1: powerful artificial intelligence tools, but his lab also has one
Speaker 1: full ability to use computers to enhance the human experience.
Speaker 1: I was struck by a remarkable video on YouTube created
Speaker 1: by his team at Berkeley. So this was a project
Speaker 1: that actually was done by my students who didn't even
Speaker 1: think of this as anything but a silly little toy
Speaker 1: project of trying to see if we could get a
Speaker 1: geeky computer science student to move like a ballerina. In
Speaker 1: the video, one of the students, Carolyn cham dances with
Speaker 1: a skill and grace of a professional despite never having
Speaker 1: studied ballet. The idea is, you take a source actor
Speaker 1: like a ballerina. There is a way to detect the
Speaker 1: limbs of the dancer, have a kind of a skeleton extracted,
Speaker 1: and also have my student just move around and do
Speaker 1: some geeky moves. And now we're basically just going to
Speaker 1: try to sympathize the appearance of my student driven by
Speaker 1: the skeleton of the ballerina. Put it all together, and
Speaker 1: then we have our grad student dancing pirouets like a ballerina.
Speaker 1: Through artificial intelligence, Carolyn's body is puppeteered by the dancer.
Speaker 1: We weren't even going to publish it, but we just
Speaker 1: released a video on YouTube called Everybody Dance Now, and
Speaker 1: somehow it really touched the nerve. Well, there's been an
Speaker 1: explosion recently a new ways to manipulate media. Alexei notes
Speaker 1: that the idea itself isn't new, It has a long history.
Speaker 1: I can't help but ask, given that you come from Russia.
Speaker 1: One of the premier users of doctoring photographs I think
Speaker 1: was Stalin, who used the ability to manipulate images for
Speaker 1: political effect. How did they do that? Can you think
Speaker 1: of examples of this and like what was the technology? Then?
Speaker 1: The urge to change photographs has been around basically since
Speaker 1: the invention of photography. For example, there is a photograph
Speaker 1: of Abraham Lincoln that still hangs in many classrooms. That's fake.
Speaker 1: It's actually Calhoun with Lincoln's head attached to it. Alexei's
Speaker 1: referring to John C. Calhoun, the South Carolina senator and
Speaker 1: champion of slavery. A Civil War portrait artist superimposed a
Speaker 1: photo of Lincoln's head onto an engraving of Calhoun's body
Speaker 1: because he thought Lincoln's gangly frame wasn't dignified enough, and
Speaker 1: so they just said Okay, we can use Calhoun. Let's
Speaker 1: slap the Lincoln's head on his body. And then, of course,
Speaker 1: as soon as you go into the twentieth century, as
Speaker 1: soon as you get to dictatorships, this is a wonderful
Speaker 1: toy for a dictator to use. So again, Stalin was
Speaker 1: big fan of this. He would get rid of people
Speaker 1: in photographs once they were out of favor, or once
Speaker 1: they got jailed or killed. He would just basically get
Speaker 1: them scratched out with reasonably crude techniques. Hitler did it,
Speaker 1: Mao did it, Castro did it, Bresnev did it. I'm
Speaker 1: sure US agencies have done it. Also, we have always
Speaker 1: manipulated images with a desire to change history. This is
Speaker 1: Honi f Reed. He's also a professor at Berkeley and
Speaker 1: a friend of Alexey's. I'm a professor of computer science
Speaker 1: and I'm an expert in digital forensics, where Alexei works
Speaker 1: on making synthetic media. Honey has devoted his career to
Speaker 1: identifying when synthetic media is being used to fool people,
Speaker 1: that is, spotting fakes. He regularly collaborates on this mission
Speaker 1: with Alexey so I, met Alyosha efros Ten, twenty years ago.
Speaker 1: He is really incredibly creative and clever guy, and he
Speaker 1: has done what I consider some of the most interesting
Speaker 1: work in computer vision and computer graphics over the last
Speaker 1: two decades. And if you really want to do forensics, well,
Speaker 1: you have to partner with somebody like Aliosha. You have
Speaker 1: to partner with a world class mind who knows how
Speaker 1: to think about the synthesis side so that you can
Speaker 1: synthesize the absolute best content and then think about how
Speaker 1: to detect it. I think it's interesting that if you're
Speaker 1: somebody on the synthesis side and developing the forensic there's
Speaker 1: a little bit of a jekylin hide there, and I
Speaker 1: think it's really fascinating. You know, the idea of altering photos,
Speaker 1: it's not entirely new. How far back does this go?
Speaker 1: So we used to have in the days of Stalin,
Speaker 1: highly talented, highly skilled, time consuming, difficult process of manipulating images,
Speaker 1: removing somebody, erasing something from the image, splicing faces together.
Speaker 1: And then we moved into the digital age where now
Speaker 1: a highly talented digital artist could remove one face and
Speaker 1: add another phase, but it was still a time consuming
Speaker 1: and required scale. In nineteen ninety four, The makers of
Speaker 1: the movie Forrest Gump won an Oscar for Visual Effects
Speaker 1: for their representations of the title character interacting with historical
Speaker 1: figures like President John F. Kennedy gratulating how do the
Speaker 1: field being all Americans? It's very good congratulation. How do
Speaker 1: you feel I got? I believe that he had. Now
Speaker 1: computers are doing all of the heavy lifting of what
Speaker 1: used to be relegated to talented artists. The average person
Speaker 1: now can use sophisticated technology to not just capture the recording,
Speaker 1: but also manipulate it and then distribute it. The tools
Speaker 1: used to create synthetic media have grown by leaps and bounds,
Speaker 1: especially in the past few years, and so now we
Speaker 1: have technology broadly called deep fake, but more specifically should
Speaker 1: be called synthesized content, where you point an image or
Speaker 1: a video or an audio to an AI or machine
Speaker 1: learning system and it will replace the face for you.
Speaker 1: I mean it can do that in an image, it
Speaker 1: can do that in a video, or it can synthesize
Speaker 1: audio for you in a particular person's voice. It's becomes
Speaker 1: straightforward to swap people's faces. There's a popular YouTube video
Speaker 1: that features tech pioneer Elon Musk's adult face on a
Speaker 1: baby's body, and there's a famous meme where actor Nicholas
Speaker 1: Cage's face replaces those of leading movie actors, both male
Speaker 1: and female. You can put words into people's mouths and
Speaker 1: make them jump and dance and run. You can even
Speaker 1: resurrect powerful figures and have them deliver a fake speech
Speaker 1: about a fake tragedy. From an Altered History, Chapter two,
Speaker 1: Creating Nixon. The text of Nixon's Moon disaster speech that
Speaker 1: we heard at the top of the show is actually
Speaker 1: not fake. As I mentioned, it was written for President
Speaker 1: Nixon as a contingency speech and thankfully never had to
Speaker 1: be delivered. It's an amazing piece of writing. It was
Speaker 1: written by Bill Safire, who was one of Nixon's speech writers.
Speaker 1: This is artist in journalist Francesca Panetta. She's the co
Speaker 1: director of the Nixon Fake or MIT's Moon Disaster Team.
Speaker 1: She's also the creative director in MIT's Center for Advanced Virtuality.
Speaker 1: I was doing experimental journalism at the Guardian newspaper. I
Speaker 1: ran the Guardians Virtual Reality studio for the last three years.
Speaker 1: The second half of the Moon Disaster team is sound
Speaker 1: artist Halsey Bergund. My name is Halsey Bergund. I am
Speaker 1: a sound artist and technologist, and I've had a lot
Speaker 1: of experience with lots of sorts of audio enhanced with technology,
Speaker 1: though this is my first experience with synthetic media, especially
Speaker 1: since I typically focus on authenticity of voice and now
Speaker 1: I'm kind of doing the opposite. So together, Halsey and
Speaker 1: Francesca chose to automate a tragic moment in history that
Speaker 1: never actually happened. I think it all started with it
Speaker 1: being the fiftieth anniversary of the moon landing last year,
Speaker 1: and add on top of that an election cycle in
Speaker 1: this country, and dealing with this information, which is obviously
Speaker 1: very important in election cycles. It was like lightbulbs went
Speaker 1: on and we got very excited about pursuing it. It's
Speaker 1: possible to make mediocre fakes pretty quickly and cheaply, but
Speaker 1: Francesca and Halsey wanted high production values. So how does
Speaker 1: one go about making a first rate fake presidential address?
Speaker 1: There are two components. There's the visuals and there's the audio,
Speaker 1: and they are completely different processes. So we decided to
Speaker 1: go with a video dialogue replacement company called Kenny Ai,
Speaker 1: who would do the visuals for us and then we
Speaker 1: decided to go with re Speech, who are a dialogue
Speaker 1: replacement company for the voice of Nixon. They tackled the
Speaker 1: voice first, the more challenging of the two mediums. What
Speaker 1: we were told to do was to get two to
Speaker 1: three hours worth of Nixon talking. That was pretty easy
Speaker 1: because the Nixon Library has hours and hours of Nixon,
Speaker 1: mainly giving Vietnam's speeches. The Communist armies of North Vietnam
Speaker 1: launched a massive inversion of South Vietnam. That audio was
Speaker 1: then chopped up into chunks between one and three seconds long.
Speaker 1: We found this incredibly patient actor called Lewis D. Wheeler.
Speaker 1: Lewis would listen to the one second clip and then
Speaker 1: he would repeat that and do what I believe was right.
Speaker 1: Re Speech would say to us things like we need
Speaker 1: to change the diagonal attention, which meant nothing to us. Yes,
Speaker 1: we have a whole lot of potential band name going forward. Yeah,
Speaker 1: Synthetic Nixon is another good one. So once we have
Speaker 1: our Nixon model made out of these thousands of tiny clips,
Speaker 1: it means that whatever our actor says will come out
Speaker 1: then in Nixon's voice. So then what we did was
Speaker 1: record the contingency speech of Nixon, and it meant that
Speaker 1: we got Lewis's actually performance but in Nixon's voice. What
Speaker 1: about the video part? I mean, the video was much easier.
Speaker 1: We're talking a couple of days here and a tiny
Speaker 1: amount of data just with Lewis's iPhone. We filmed him
Speaker 1: reading the contingency speech once a couple of minutes of
Speaker 1: him just chatting to camera, and that was it fate
Speaker 1: that the men who went to the Moon to explore
Speaker 1: in peace will stay on. You know. We were told
Speaker 1: by Kenny Ai that everything would be the same in
Speaker 1: the video apart from just the area around the mouth.
Speaker 1: So every gesture of the hand, every blink, every time
Speaker 1: he moved his face, all of that would stay the same,
Speaker 1: but just the mouth basically would change. So we used
Speaker 1: Nixon's resignation speech to have served in this office, it's
Speaker 1: to have felt a very personal sense of it was
Speaker 1: the speech of Nixon that looked the most somber, where
Speaker 1: he seemed to have the most emotion in his face.
Speaker 1: So what actually went on in the computer? Artificial intelligence
Speaker 1: sometimes sounds inscrutable, but the basic ideas are quite simple.
Speaker 1: In this case, it uses a type of computer program
Speaker 1: called an auto encoder. It's trained to take complicated things,
Speaker 1: say spoken sentences or pictures, encode them in a much
Speaker 1: simpler form, and then decode them to recover the original
Speaker 1: as bested. Care the encoder tries to reduce things to
Speaker 1: their essence, throwing away most of the information but keeping
Speaker 1: enough to do a good job of reconstructing it to
Speaker 1: make a deep fake. Here's the trick. Train a speech
Speaker 1: auto encoder for Nixon to Nixon, and a speech auto
Speaker 1: encoder for actor to actor, but force them to use
Speaker 1: the same encoder. Then you can input actor and decoded
Speaker 1: as Nixon. If you have enough data. It's a piece
Speaker 1: of cake around there. Carefully created video, the Moon Disaster
Speaker 1: team created an entire art installation a nineteen sixties living
Speaker 1: room with a fake vintage newspaper sharing the fake tragic
Speaker 1: news while a fake Nixon speaks solemnly on a vintage
Speaker 1: black and white television. Some people, when they were watching
Speaker 1: the installation, they watched a number of times. You'd see them,
Speaker 1: they'd watch at once, then they would watch it again,
Speaker 1: staring at the lips to see if they could see
Speaker 1: any lack of synchronicity. We had some people who thought
Speaker 1: that perhaps Nixon had actually recorded this speech as a
Speaker 1: contingency speech for it to go onto television. Lots of
Speaker 1: folks who were listening, viewing, and even press folks just
Speaker 1: immediately said, oh, the voice is real or whatever you
Speaker 1: said these things that weren't accurate because they just felt
Speaker 1: like there wasn't even a question. I suppose that is
Speaker 1: what we wanted to achieve, But at the same time,
Speaker 1: it was a little bit eye opening and like a
Speaker 1: little scary. You know that that could happen. Chapter three,
Speaker 1: Everybody dance. What do you see as just the wonderful
Speaker 1: upside of having technologies like this? Yeah, I mean a
Speaker 1: aion art is becoming a whole field in itself, so creatively,
Speaker 1: there is enormous potential. One of the potential positive educational
Speaker 1: uses of deep fake technology would be to bring historical
Speaker 1: figures back to life to make learning more durable. I
Speaker 1: think one could do that with bringing Abraham Lincoln back
Speaker 1: to life and having him deliver speeches. Film companies are
Speaker 1: really excited about re enactments. We're already beginning to see
Speaker 1: this in films like Star Wars, when we're bringing people
Speaker 1: like Carrie Fisher back to life. I mean that is
Speaker 1: at the moment not being done through deep fake technologies.
Speaker 1: This is using fatty traditional techniques of CGI at the moment,
Speaker 1: So we still have to see our first deep fake
Speaker 1: big cinema screen release. But this is just to come
Speaker 1: like the technology is getting better and better. Not only
Speaker 1: will we be able to potentially bring back actors and
Speaker 1: actresses who are no longer alive and have them star
Speaker 1: in movies, but an actor could make a model of
Speaker 1: their own voice and then sell the use of that
Speaker 1: voice to anybody to do a voiceover of whatever is wanted,
Speaker 1: and so they could have twenty of the is going
Speaker 1: on at the same time, and the sort of restriction
Speaker 1: of their physical presence is no longer there. And that
Speaker 1: might mean that, you know, Brad Pitt is in everything,
Speaker 1: or it might just mean that lower budget films can
Speaker 1: afford to have some of the higher cost talent. At
Speaker 1: that point, you know, the top twenty actors could just
Speaker 1: do everything. Yes, there's no doubt that there will be
Speaker 1: winners and losers from these technologies, but the potential of
Speaker 1: synthetic media goes way beyond the arts. There are possible
Speaker 1: medical and therapeutic applications. There are companies that are working
Speaker 1: very hard to allow people who have either lost their
Speaker 1: voice or who never had a voice, to be able
Speaker 1: to speak in a way that is either how they
Speaker 1: used to speak or in a way that isn't a
Speaker 1: canned voice that everybody has. Alexei ePROs and his students
Speaker 1: discovered potential uses of synthetic media and medicine quite unintentionally
Speaker 1: while working on their Everybody Dance Now project that could
Speaker 1: turn anyone into a ballerina. Were kind of surprised for
Speaker 1: all the positive feedback we got. We've got emails from
Speaker 1: people who were quadriplegic and they asked us if we
Speaker 1: could make them dance, and it was very unexpected. So
Speaker 1: now we are trying to get the software to be
Speaker 1: in a state where people can use it, because yeah,
Speaker 1: it's somehow it did hit a nerve with folks. Chapter
Speaker 1: four Unicorns in the Andes. The past few years have
Speaker 1: seen amazing advances in the creation of synthetic media through
Speaker 1: artificial intelligence. The technology now goes far beyond fitting one
Speaker 1: face over another face in a video. A recent breakthrough
Speaker 1: has made it possible to create entirely new and very
Speaker 1: convincing content out of thin air. The breakthrough called generative
Speaker 1: adversarial networks or GAMS, came from a machine learning researcher
Speaker 1: at Google named Ian Goodfellow. Like auto encoders, the basic
Speaker 1: idea is simple but brilliant. Suppose you want to create
Speaker 1: amazingly realistic photos of people who don't exist. While you
Speaker 1: build a GAN consisting of two computer programs, a photo
Speaker 1: generator that learns to generate fake photos and a photo
Speaker 1: discriminator that learns to discriminate or identify fake photos from
Speaker 1: a vast collection of real photos. You then let the
Speaker 1: two programs compete, continually tweaking their code to outsmart each other.
Speaker 1: By the time they're done, the GAN can generate amazingly
Speaker 1: convincing fakes. You can see for yourself if you go
Speaker 1: to the website this Person does Not Exist dot com.
Speaker 1: Every time you refresh the page, you're shown a new
Speaker 1: uncanny image of a person who, as the website says,
Speaker 1: does not and never did exist. Francescan I actually tried
Speaker 1: out the website. This young Asian woman. She's got great complexion.
Speaker 1: Envious of that neat black hair with a fringe pink
Speaker 1: lipstick and a slightly dreamy look as she's kind of
Speaker 1: gazing off to her left. Oh, here's a woman who
Speaker 1: looks like she could be a neighbor of mine in Cambridge,
Speaker 1: probably about sixty five. She's got nice wire framed glasses,
Speaker 1: layered hair. Her earrings don't actually match, but that could
Speaker 1: just be her distinctive style. I mean, of course, she
Speaker 1: doesn't really exist. It's hard to argue that gams aren't
Speaker 1: creating original art. In fact, an artist collective recently used
Speaker 1: a GAM to create a French Impressionist style portrait. When
Speaker 1: Christie's sold it at auction, it fetched an eye popping
Speaker 1: four hundred and thirty two thousand dollars. Alexei Efros, the
Speaker 1: Berkeley professor, recently pushed gans a step further, creating something
Speaker 1: called cycle gans. By connecting two gans together in a
Speaker 1: clever way, cycle gans can transform a monet painting into
Speaker 1: what's seemingly a photograph of the same scene, or turn
Speaker 1: a summer landscape into a winter landscape of the same view.
Speaker 1: Alexei's cycle gans seem like magic. If you were to
Speaker 1: add in virtual reality, the possibilities become mind blowing. You
Speaker 1: may be reminiscing about walking down Saint German and Paris
Speaker 1: and with a few clicks. You are there, and you're
Speaker 1: walking down the boulevard, and you're looking at all the buildings,
Speaker 1: and maybe you can even switch to a different year.
Speaker 1: And I think that is I think very exciting as
Speaker 1: a way to mentally travel to different places. So if
Speaker 1: you do this in VR, I mean, can you imagine
Speaker 1: classes going on a class visit to ancient Rome. That's right,
Speaker 1: you could imagine from how a particular city like Chrome.
Speaker 1: Luke's now trying to extrapolate to how it looked in
Speaker 1: the past. It turns out that gans aren't just transforming images.
Speaker 1: I spoke with a friend who's very familiar with another
Speaker 1: remarkable application of the technology. My name is Reid Hoffman.
Speaker 1: I'm a podcaster of Master's Scale. I'm a partner at Greylock,
Speaker 1: which is where we're sitting right now, co founder of LinkedIn,
Speaker 1: and then a variety of other eccentric hobbies. Reid is
Speaker 1: a board member of an unusual organization called open AI.
Speaker 1: Open a Eyes is highly concerned with artificial general intelligence
Speaker 1: human level intelligence. I helped Sam Altman and Elon Musk
Speaker 1: standing up. The basic concern was that if one company
Speaker 1: created and deployed that that could be is balancing in
Speaker 1: all kinds of ways. And so the thought is, if
Speaker 1: it could be created, we should make sure that there
Speaker 1: is essentially a nonprofit that is creating this and that
Speaker 1: can make that technology available at selective time, slices to
Speaker 1: industry as a whole, government, etc. Last year, open ai
Speaker 1: released a program that uses gams to write language from
Speaker 1: a short opening prompt. The system, called GPT two, can
Speaker 1: spin a convincing article or story instead of a deep
Speaker 1: fake video. It's deep fake text. It's pretty amazing actually.
Speaker 1: For example, open ai researchers gave the program the following prompt.
Speaker 1: In a shocking finding, scientists discovered a herd of unicorns
Speaker 1: living in a remote, previously unexplored valley in the Andes Mountains.
Speaker 1: Even more surprising to the researches was the fact that
Speaker 1: the unicorns spoke perfect to English. GPT two took it
Speaker 1: from there, the livering nine crisp paragraphs on the landmark discovery.
Speaker 1: I asked Franz to read a bit from the story.
Speaker 1: Doctor Jorge Perez, an evolutionary biologists from the University of
Speaker 1: Lapez and several companions, were exploring the Andes Mountains when
Speaker 1: they found a small valley with no other animals or humans.
Speaker 1: Perez noticed that the valley had what appeared to be
Speaker 1: a natural fountains surrounded by two peaks of rock and
Speaker 1: silver snow. Perez and the others then ventured further into
Speaker 1: the valley. By the time we reached the top of
Speaker 1: one peak, the water looked blue with some crystals on top,
Speaker 1: said Perez. Perez and his friends were astonished to see
Speaker 1: the unicorn. Heard. Tell me some of the great things
Speaker 1: you can do with language generation, well, say, for example, entertainment,
Speaker 1: generate stories that could be fresh and interesting and new
Speaker 1: and personal for every child. Embed educational things in those
Speaker 1: stories of the on into the fact that the story
Speaker 1: is involving them and their friends, but also now brings
Speaker 1: in grammar and math and other kinds of things as
Speaker 1: the doing it generate explanatory material of this kind of
Speaker 1: education that works best for this audience, for this kind
Speaker 1: of people, like we want to have this kind of
Speaker 1: math or this kind of physics, or this kind of
Speaker 1: history or this kind of poetry explained in the right way,
Speaker 1: and also the style of language right like you know
Speaker 1: native city x language. When open ai announced its breakthrough
Speaker 1: program for text generation, it took the unusual step of
Speaker 1: not releasing the full powered version because it was worried
Speaker 1: about the possible consequences. Now, part of the open AI
Speaker 1: decision to say we're going to release a smaller model
Speaker 1: than the one we did is because we think that
Speaker 1: the deep fake problem hasn't been solved. And by the way,
Speaker 1: some people complained about that, because they said, well, you're
Speaker 1: slowing down our ability to do progress. And so for
Speaker 1: the answer and say, look, when these are at least
Speaker 1: to the entire public, we cannot control the downside as
Speaker 1: well as upsides. Downsides from art to therapy to virtual
Speaker 1: time travel, personalized stories and education, synthetic media has amazing upsides.
Speaker 1: What could possibly go wrong? Chapter five? What could possibly
Speaker 1: go wrong? The downsides are actually not hard to find.
Speaker 1: The ability to reshape reality brings extraordinary power, and people
Speaker 1: inevitably use power to control other people. It should be
Speaker 1: no surprise, therefore, that ninety six percent of fake videos
Speaker 1: posted online are non consensual pornography videos, almost always of
Speaker 1: women manipulated to depict sex acts that never actually occurred.
Speaker 1: I spoke with a professor who studies deep fakes, including
Speaker 1: digital attempts to control women's bodies. I'm Danielle Citron and
Speaker 1: I am a law professor at Boston University School of Law.
Speaker 1: I write about privacy, technology, automation. My newest work and
Speaker 1: my next book is going to be about sexual privacy.
Speaker 1: So I've worked in and around consumer privacy, individual rights,
Speaker 1: civil rights. I write a lot about free speech and
Speaker 1: then automated systems. When do you first become aware of
Speaker 1: deep fakes? Do you remember when this cross rit I did? So,
Speaker 1: there was a Reddit thread devoted to, you know, fake
Speaker 1: pornography movies of Gal Jadot Emma Watson. But the reddit
Speaker 1: thread sort of spooled not just from celebrities but ordinary people,
Speaker 1: and so you had rereditors asking each other, how do
Speaker 1: I make a deep fake sex video of max girlfriend?
Speaker 1: I have thirty pictures? And then other redditors would chime
Speaker 1: in and say, look at this YouTube tutorial. You can
Speaker 1: absolutely make a deep fake sex video of your ex
Speaker 1: with thirty pictures. I've done it with twenty. In November
Speaker 1: two thousand seventeen, an anonymous reditor began posting synthesized porn
Speaker 1: videos under the pseudonym deep fakes, perhaps a nod to
Speaker 1: the deep learning technology used to create them as well
Speaker 1: as the nineteen seventies porn film deep Throat. The Internet
Speaker 1: quickly adopted the term deep fakes and broadened its meanings
Speaker 1: beyond pornography. To create the videos, he used celebrity faces
Speaker 1: from Google image search and YouTube videos and then trains
Speaker 1: an algorithm on that content together with pornographic videos. Have
Speaker 1: you seen deep fake pornography videos? Yes, so still pretty crude,
Speaker 1: so you probably can tell that it's a fake, but
Speaker 1: for the person who's inserted into pornography, it's devastating. You
Speaker 1: use the neural network technology, the artificial intelligence technology to
Speaker 1: create out of digital whole cloth pornography videos using proba
Speaker 1: real pornography and then inserting the person in the pornography
Speaker 1: so they become the female actress. If it's a female,
Speaker 1: it's usually a female in that video. My name is
Speaker 1: Noel Martin and I am an activist and Laura Form
Speaker 1: campaigner in Australia. Noel is twenty six years old and
Speaker 1: she lives in Perth, Australia. So the first time that
Speaker 1: I discovered myself on pornographic sites was when I was
Speaker 1: eighteen and out of curiosity, decided to Google image reverse
Speaker 1: search myself in an instant, like in a less than
Speaker 1: a millisecond, my life completely changed. At first, it started
Speaker 1: with photos still images stolen from Noel's social media accounts.
Speaker 1: They were then doctoring my face from ordinary images and
Speaker 1: superimposing those onto the bodies of women depicting me having
Speaker 1: sexual intercourse. It proved impossible to identify who was manipulating
Speaker 1: Nowell's image in this way. It's still unclear today, which
Speaker 1: made it difficult for her to seek legal action. I
Speaker 1: went to the police soon after, I contacted government agencies,
Speaker 1: tried getting a private investigator. Essentially, there's nothing that they
Speaker 1: could do. The sites are hosted overseas, the perpetrators are
Speaker 1: probably overseas. The reaction was at the end of the day,
Speaker 1: I think you can contact the webmasters to try and
Speaker 1: get things deleted. You know, you can adjust your privacy
Speaker 1: setting so that nothing is available to anyone publicly. It
Speaker 1: was an unwinnable situation. Then things started to escalate. In
Speaker 1: twenty eighteen, who Well saw a synthesized pornographic video of
Speaker 1: herself and I believe that it was done for the
Speaker 1: purposes of silencing me because I've been very public about
Speaker 1: my story and advocating for change. So I had actually
Speaker 1: gotten email from a fake email address, and you know,
Speaker 1: I clicked the link. I was actually at work. It
Speaker 1: was a video of me having sexual intercourse. The title
Speaker 1: had my name, the face of the woman in it
Speaker 1: was edited so that it was my face, and you know,
Speaker 1: all the tags were like Noel Martin Australia, feminist, and
Speaker 1: it didn't look real, but the context of everything with
Speaker 1: the title my face, with the tags all points to
Speaker 1: me being depicted in this video. The fakes were of
Speaker 1: poor quality, but poor and consumers are in a discriminating lot,
Speaker 1: and many people reacted to them as if they were real.
Speaker 1: The public reaction was horrifying to me. I was a victim,
Speaker 1: blamed and slut shamed, and it's definitely limited the course
Speaker 1: of where I can go in terms of career and employment.
Speaker 1: Noel finished a degree in law and began campaigning to
Speaker 1: criminalize this sort of content. My advocacy and my activism
Speaker 1: started off because I had a lived experience of this,
Speaker 1: and I experienced it at a time where it wasn't
Speaker 1: criminalized in Australia. The distribution of altered intimate images or
Speaker 1: altered intimate videos and so I had to petition, meet
Speaker 1: with my politicians in my area. I wrote a number
Speaker 1: of articles, I spoke to the media, and I was
Speaker 1: involved in the law reform in Australia in a number
Speaker 1: of jurisdictions in Western Australia and New South Wales, and
Speaker 1: I ended up being involved in two press conferences with
Speaker 1: the Attorney generals of each state at the announcement of
Speaker 1: the law that was criminalizing this abuse. Today, in part
Speaker 1: because of Noel's activism, it is illegal in Australia to
Speaker 1: distribute intimate images without consent, including intimate images and videos
Speaker 1: that have been altered. Although it doesn't encompass all malicious
Speaker 1: synthetic media, Noel has made a solid start. Chapter six,
Speaker 1: Scissors and Glue. The videos depicting Noel Martin were nowhere
Speaker 1: near as sophisticated as those made by the Moon Disastered team.
Speaker 1: They were more cheap fakes than deep fakes, and yet
Speaker 1: the point didn't have to be perfect to be devastating.
Speaker 1: The same turns out to be true in politics. To
Speaker 1: understand the power of fakes, you have to understand human psychology.
Speaker 1: It turns out that people are pretty easy to fool.
Speaker 1: Carry I was running for President of the US. His
Speaker 1: stance on the Vietnam War was controversial. Jane Fonda, of course,
Speaker 1: was a very controversial figure back then because of her
Speaker 1: anti war stand. What have we become as a nation
Speaker 1: if we call the men heroes that were used by
Speaker 1: the Pentagon to try to exterminate an entire people? What
Speaker 1: business have we to try to exterminate a people? And
Speaker 1: somebody had created a photo of the two of them
Speaker 1: sharing a stage and an anti war rally with the
Speaker 1: hopes of damaging the Carry campaign. The photo was fake.
Speaker 1: They had never shared a stage together. They just took
Speaker 1: two images, probably put it into some standard photo editing
Speaker 1: software like a Photoshop, and just put a headline around it,
Speaker 1: and out to the world it went. And I will
Speaker 1: tell you I remember the most fascinating interview I've heard
Speaker 1: in a long time was right after the election, Kerry
Speaker 1: of course lost, and a voter was being interviewed and
Speaker 1: asked how they voted, and he said he couldn't vote
Speaker 1: for Carry, and the interview said, well why not? And
Speaker 1: the gentleman said, I couldn't get that photo of John
Speaker 1: Carry and Jane Fonda out of my head. And the interviews, well,
Speaker 1: you know, that photo is fake, and the guy said,
Speaker 1: much to my surprise, yes, but I couldn't get it
Speaker 1: out of my mind. And this shows you the power
Speaker 1: of visual imagery, Like even after I tell you something
Speaker 1: is fake, it still had an impact on somebody, and
Speaker 1: I thought, Wow, we're in a lot of trouble because
Speaker 1: it's very very hard to put the cat back into
Speaker 1: the bag. Once that content is out there, you can't
Speaker 1: undo it. So seeing is believing, even above thinking Yeah,
Speaker 1: that seems to be the rule. There is very good
Speaker 1: evidence from the social science literature that it's very very
Speaker 1: difficult to correct the record after the mistakes are out there.
Speaker 1: Law professor Danielle Citram also notes that humans tend to
Speaker 1: pass on information without thinking, which triggers what she calls
Speaker 1: information cascades. Information cascades is a phenomenon where we have
Speaker 1: so much information overload that when someone sends us something,
Speaker 1: some information, and we trust that person, we pass it on.
Speaker 1: We don't even check it's veracity, and so information can
Speaker 1: go viral fairly quickly because we're not terribly reflective, because
Speaker 1: we act on impulse. Danielle says that information cascades have
Speaker 1: been given new life in the twenty first century through
Speaker 1: social media. Think about the twentieth century phenomenon, where do
Speaker 1: we get most of our information from trusted sources, trusted newspapers,
Speaker 1: trusted major couple of TV channels. Growing up, we only
Speaker 1: had you know, we didn't have a million, and they
Speaker 1: were adhering to journalistic ethics and commitments to truth and
Speaker 1: neutrality and notion that you can't publish something without checking it.
Speaker 1: Now we are publishing information that most people say. We're
Speaker 1: lying on our peers and our friends. Social media platforms
Speaker 1: are designed to tailor our information diet to what we
Speaker 1: want and to our pre existing views, so we're locked
Speaker 1: in a digital echo chamber. We think everybody agrees with us.
Speaker 1: We pass on that information. We haven't checked the veracity.
Speaker 1: It goes wild and we're especially likely to pass it
Speaker 1: on if it's negative and novel. Why's that? It's just
Speaker 1: like it's one of our weaknesses. We know how gossip
Speaker 1: goes like wildfire online. So like Hillary Clinton as running
Speaker 1: a sex ring. That's crazy. Oh my god, Eric, did
Speaker 1: you hear about that. I'll post it on Facebook. Eric,
Speaker 1: you pass it on. We just can't help ourselves, and
Speaker 1: it is much in the way that we love suits
Speaker 1: and fats and pizza. You know, we indulge. We don't think.
Speaker 1: On some sense, this phenomenon is an old phenomenon. Right
Speaker 1: is the famous observation by Mark Twain about how a
Speaker 1: lie gets halfway around the world before the truth gets
Speaker 1: its pants. Hall. Yeah, the truth still in the bedroom
Speaker 1: getting dressed, and we often will see the lie, but
Speaker 1: the rebuttal is not seen. It's often lost in the
Speaker 1: noise ways of the defamatory statements. That is not new.
Speaker 1: But what is new is a number of things about
Speaker 1: our information ecosystem are our force multipliers Chapter seven, Truth Decay.
Speaker 1: Many experts are worried that the rapid advances in making fakes,
Speaker 1: combined with a catalyst of information cascades, will undermine democracy.
Speaker 1: The biggest concerns have focused on elections Globally, we are
Speaker 1: looking at highly polarized situations where this kind of manipulated
Speaker 1: media can be used as a weapon. One of the
Speaker 1: main reasons Francesca and Halsey made their Nixon deep fake
Speaker 1: was to spread awareness about the risks of misinformation campaigns
Speaker 1: before the twenty twenty US presidential election. Similarly, a group
Speaker 1: showcased the power of deep fakes by making videos the
Speaker 1: run up to the UK parliamentary election showing the two
Speaker 1: bitter rivals, Boris Johnson and Jeremy Corman, each endorsing the other.
Speaker 1: I wish to rise above this divide and indorse my
Speaker 1: worthy opponent, the right Honorable Jeremy Corbyn. SIPI Prime Minister
Speaker 1: of our United Kingdom, back Boris Johnson to continue as
Speaker 1: our Prime Minister. But you know what, don't listen to me.
Speaker 1: I think I may be one of the thousands of
Speaker 1: deep fakes on the Internet, using powerful technologies to tell
Speaker 1: stories that aren't so. This just kind of indicates how
Speaker 1: candidates and political figures can be misrepresented, and you just
Speaker 1: need to feed them into people's social media feeds for
Speaker 1: them to be seeing this at times when the stakes
Speaker 1: are pretty high. So far, we haven't yet seen sophisticated
Speaker 1: deep fakes in US or UK politics. That might be
Speaker 1: because fakes will be most effective if they're tim for
Speaker 1: XM chaos, say close to election day, when newsrooms won't
Speaker 1: have the time to investigate and debunk them. But another
Speaker 1: reason might be the cheap fakes made with basic video
Speaker 1: editing software are actually pretty effective. Remember the video that
Speaker 1: surfaced of how speaker Nancy Pelosi, in which she appeared
Speaker 1: intoxicated and confused. We want to give this president the
Speaker 1: opportunity to something historic for our country. Both President Trump
Speaker 1: and Rudy Giuliani shared the video as fact on Twitter.
Speaker 1: The video is just a cheap fake, just slowed down
Speaker 1: Pelosi's speech to make her seem incompetent. But maybe elections
Speaker 1: won't be the biggest targets. Some people worry that deep
Speaker 1: fakes could be weaponized to foment international conflict. Berkeley professor
Speaker 1: Honey f Reed has been working with US government's Media
Speaker 1: Forensics program to address this issue. DARPA, the Defense Department's
Speaker 1: research arm, has been pouring a lot of money over
Speaker 1: the last five years into this program. They are very
Speaker 1: concerned about how this technology can be a threat to
Speaker 1: national security and also how when we get images and
Speaker 1: videos from around the world in areas of conflict, do
Speaker 1: we know if they're real or not? Is this really
Speaker 1: an image of a US soldier who has been taken hostage?
Speaker 1: How do we know? So? What do you see as
Speaker 1: some of the worst case scenarios. Here's the things that
Speaker 1: keep me up at night. Right. A video of Donald
Speaker 1: Trump saying I've launched nuclear weapons against Iran, and before
Speaker 1: anybody gets around to figuring out whether this is real
Speaker 1: or not, where we have global nuclear moutdown. And here's
Speaker 1: the thing. I don't think that that's likely, but I
Speaker 1: also don't think that the probability of that is zero.
Speaker 1: And that should worry us because while it's not likely,
Speaker 1: the consequences are spectacularly bad. Lawyer Danielle Citram worries about
Speaker 1: an even more plausible scenario. And imagine a deep fake
Speaker 1: of a well known American general burning a koran and
Speaker 1: it is timed at a very tense moment in a
Speaker 1: particular most you know country, whether it's Afghanistan. It could
Speaker 1: then lead to physical violence. And you think this could
Speaker 1: be made. No general, no qoran actually used in the video,
Speaker 1: just programmed. You can use the technology to mine existing photographs.
Speaker 1: Kind of easy, especially with someone like take Jim Mattis
Speaker 1: when he was our defense secretary. Of Jim Mattis, you know,
Speaker 1: actually taking a koran and ripping it in half and
Speaker 1: say all Muslims should die. Imagine the chaos in diplomacy,
Speaker 1: the chaos of our soldiers abroad in Muslim countries. It
Speaker 1: would be inciting violence without question. Well, we haven't yet
Speaker 1: seen spectacular fake videos used to disrupt elections or create
Speaker 1: international chaos. We have seen recingly sophisticated attacks on public policymaking.
Speaker 1: So we've got an example in twenty seventeen where the
Speaker 1: FEC solicited public comment on the proposal to repeal net neutrality.
Speaker 1: Net neutrality is the principle that internet service providers should
Speaker 1: be a neutral public utility. They shouldn't discriminate between websites,
Speaker 1: say slowing down Netflix streaming to encourage you to purchase
Speaker 1: a different online video service. As President Barack Obama described
Speaker 1: in twenty fourteen, there are no gatekeepers deciding which sites
Speaker 1: you get to access. There are no toll roads on
Speaker 1: the information super Highway. Federal Communications Policy had long supported
Speaker 1: net neutrality, but in twenty seventeen, the Trump administration favored
Speaker 1: repealing the policy. There were twenty two million comments that
Speaker 1: the FEC received, but ninety six percent of those were
Speaker 1: actually fake. The interesting thing is the real comments were
Speaker 1: opposed to repeal, whereas the fake comments were in favor.
Speaker 1: A Wall Street Journal investigation exposed that the fake public
Speaker 1: comments were generated by bots. It found similar problems with
Speaker 1: public comments about pay data lending. The bots varied their
Speaker 1: comments in a combinatorial fashion so that the content wasn't identical.
Speaker 1: With a little sleuthing, though, you could see that they
Speaker 1: were generated by computers. But with the technology increasingly able
Speaker 1: to generate completely original writing, like open aiyes program that
Speaker 1: wrote the story about unicorns in the Andes, it's going
Speaker 1: to become hard to spot the fakes. So there was
Speaker 1: this Harvest student, Max Weiss, who used GPT two to
Speaker 1: kind of demonstrate this, And I went on his site
Speaker 1: yesterday and he's got this little test where you need
Speaker 1: to decide whether a comment is real or fake. So
Speaker 1: you go on and you read it and you decide
Speaker 1: whether it's been written by a bot or by a human.
Speaker 1: So I did this, and the ones that seemed to
Speaker 1: be really well written and quite narrative discussive, generally I
Speaker 1: was picking them as human. I was wrong almost all
Speaker 1: the time. It was amazing and alarming. In our democracy,
Speaker 1: public comments have been an important way in which citizens
Speaker 1: can make their voices heard, but now it's becoming easy
Speaker 1: to drown out those voices with millions of fake opinions. Now,
Speaker 1: the downfall of truth likely won't come with a bang,
Speaker 1: but a whimper, a slow, steady erosion that some call
Speaker 1: truth decay. If you can't believe anything you read, or
Speaker 1: hear or see anymore, I don't know how you have
Speaker 1: a democracy a I don't know, frankly, how we have
Speaker 1: civilized society if everybody's going to live in an echo
Speaker 1: chamber believing their own version of events. How do we
Speaker 1: have a dialogue if we can't agree on basic facts.
Speaker 1: In the end, the most insidious impact of deep fakes
Speaker 1: may not be the deep fake content itself, but the
Speaker 1: ability to claim that real content is fake. It's something
Speaker 1: that Danielle Citron refers to as the liar's dividend. The
Speaker 1: liar's dividend is that the more you educate people about
Speaker 1: the phenomenon of deep fags, the more the wrongdoer can
Speaker 1: disclaim reality. Think about what President Trump did with the
Speaker 1: Access Hollywood tape. You know, I'm automatically attracted to beautiful
Speaker 1: I just started kissing them. It's like a magnet, kid.
Speaker 1: I don't even know it. And when you're started, they
Speaker 1: let you do it. You can do anything whatever you want.
Speaker 1: Grab them by the pro I can do anything. Initially,
Speaker 1: Trump apologized for the remarks. Anyone who knows me knows
Speaker 1: these words don't reflect who I am. I said it,
Speaker 1: I was wrong, and I apologize. But in twenty seventeen,
Speaker 1: a year after his initial apology and with the idea
Speaker 1: of deep fake content starting to gain attention, Trump changed
Speaker 1: his tomb upon reflection, he said, they're not real. That
Speaker 1: wasn't me. I don't think that was my voice. That's
Speaker 1: the liar's dividend. In practice, the Trump comments about excess
Speaker 1: Hollywood was remarkable. Slightly more subtle than that, he said,
Speaker 1: I'm not sure that was me. Right, Well, that's the
Speaker 1: corrosive gas lighting. Chapter eight, A Life Stored in the Cloud.
Speaker 1: Deep fakes have the potential to devastate individuals and harms society.
Speaker 1: The question is can we stop them from spreading before
Speaker 1: they get out of control. To do so, we'd need
Speaker 1: reliable ways to spot deep fakes. So the good news
Speaker 1: is there are still artifacts in the synthesized content, whether
Speaker 1: those are images, audio, or a video, that we as
Speaker 1: the experts, can tell apart. So when, for example, The
Speaker 1: New York Times wants to run a story with a video,
Speaker 1: we can help them validate it. What are the real
Speaker 1: sophisticated experts looking. Yeah, so the eyes are really wonderful
Speaker 1: forensically because they reflect back to you what is in
Speaker 1: the scene. So I'm sitting now right now in a studio,
Speaker 1: there's maybe about a dozen or so lights around me,
Speaker 1: and you can see this very complex set of reflections
Speaker 1: in my eyes. So we can analyze fairly complex lighting patterns,
Speaker 1: for example, to determine if this is one person's head
Speaker 1: spliced onto another person's body, or if the two people
Speaker 1: standing next to each other were digitally inserted from another photograph.
Speaker 1: I could spend another hour telling you about the many
Speaker 1: different forensic techniques that we've developed. There's no silver bullet here.
Speaker 1: Really is a sort of a time consuming and deliberate
Speaker 1: and thoughtful and it requires many many tools, and it
Speaker 1: requires people with a fair amount of skill to do this.
Speaker 1: Honey Freed also has quite a few detection techniques that
Speaker 1: he won't speak about publicly for fear of the deep
Speaker 1: fake creators will learn how beat his tests. I don't
Speaker 1: create a GitHub repository and give my code to all
Speaker 1: my adversaries. I don't have just one forensic techniques. I
Speaker 1: have a couple dozen of them. So that means you,
Speaker 1: as the person creating this now have to go back
Speaker 1: and implement twenty different techniques and you have to do
Speaker 1: it just perfectly, and that makes the landscape a little
Speaker 1: bit more tricky for you to manage. As technology makes
Speaker 1: it easier to create deep fakes, a big problem will
Speaker 1: be the sheer amounts of content to review. So the
Speaker 1: average person can download software repositories, and so it's getting
Speaker 1: to the point now where the average person can just
Speaker 1: run these as if they're running any standard piece of software.
Speaker 1: There's also websites that have propped up where you can
Speaker 1: pay them twenty bucks and you tell them, please put
Speaker 1: this person's face into this person's video, and they will
Speaker 1: do that for you. And so it doesn't take a
Speaker 1: lot to get access to these tools. Now, I will
Speaker 1: say that the output of those are not quite as
Speaker 1: good as what we can create inside the lab. And
Speaker 1: you just know what the trend is. You just know
Speaker 1: it's going to get better and cheaper and faster and
Speaker 1: easy to use. Detecting dow fakes will be a never
Speaker 1: ending cat and mouse game. Remember how generative adversarial networks
Speaker 1: or gams are built by training a fake generator to
Speaker 1: outsmart a detector. Well. As detectors get better, fake generators
Speaker 1: will be trained to keep pays still. Detectives like Honey
Speaker 1: and platforms like Facebook are working to develop automated ways
Speaker 1: to spot deep fakes rapidly and reliably. That's important because
Speaker 1: more than five hundred additional hours of video are being
Speaker 1: uploaded to YouTube every minute. I don't mean to sound
Speaker 1: defeatist about this, but I'm going to lose this war.
Speaker 1: I know this because it's always going to be easier
Speaker 1: to create content than it is to detect it. But
Speaker 1: here's where I will win. I will take it out
Speaker 1: of the hands of the average person. So think about,
Speaker 1: for example, the creation of counterfeit currency. With the latest
Speaker 1: innovations brought on by the Treasure Department, it is hard
Speaker 1: for the average person to take their inkjet printer and
Speaker 1: create compelling fake currency. And I think that's going to
Speaker 1: be the same trend here is that if you're using
Speaker 1: some off the shelf tool, if you're paying somebody on
Speaker 1: the website, we're going to find you, and we're going
Speaker 1: to find you quickly. But if you are a dedicated,
Speaker 1: highly skilled of the time and the effort to create it,
Speaker 1: we are going to have to work really hard to
Speaker 1: detect those. Given the challenges of detecting fake content, some
Speaker 1: people envision a different kind of techno fix. They propose
Speaker 1: developing airtight ways for content creators to mark their own
Speaker 1: original video as real. That way, we get instantly recognize
Speaker 1: an altered version if it wasn't identical. Now there's ways
Speaker 1: of authenticating at the point of recording, and these are
Speaker 1: what it's called control capture system. So here's the idea.
Speaker 1: You use a special app on your mobile device that,
Speaker 1: at the point of capture, a cryptographically signs the image
Speaker 1: of the video or the audio. It puts that signature
Speaker 1: onto the blockchain. The only thing you have to know
Speaker 1: about the blockchain is that it is an immutable distributed ledger,
Speaker 1: which means that that signature is essentially impossible to manipulate.
Speaker 1: And now all of that happened at the point of recording.
Speaker 1: If I was running a campaign today and I was
Speaker 1: worried about my candidates likeness being misused, absolutely every public
Speaker 1: event that they were at, I would record with a
Speaker 1: control capture system and I'd be able to prove what
Speaker 1: they actually said or did at any point in the future.
Speaker 1: So this approach would shift the burden of authentication so
Speaker 1: the people creating the videos rather than publishers or consumers.
Speaker 1: Law professor Danielle Citron has explored how this solution could
Speaker 1: quickly become dystopium. We might see the emergence of an
Speaker 1: essentially an audit trail of everything you do and say
Speaker 1: all of the time. Danielle refers to the business model
Speaker 1: as immutable lifelogs in the cloud. In a way we
Speaker 1: sort of already seen it. There are health plans that
Speaker 1: if you wear a fitbit all the time and you
Speaker 1: let yourself be monitored as your insurance, you know your
Speaker 1: health insurance rates. But you can see how if the
Speaker 1: incentives are there in the market to self surveil, whether
Speaker 1: it's for health insurance, life insurance, car insurance, we're going
Speaker 1: to see the unraveling of bribe to say by ourselves.
Speaker 1: You know, corporations may very well, because the CEO is
Speaker 1: so valuable, they may say you've got to have a log,
Speaker 1: an immutable audit trail of everything you do and say.
Speaker 1: So when that deep fake comes up the night before
Speaker 1: the IPO, you can say, look, the CEO wasn't taking
Speaker 1: the bribe wasn't having sex with a prostitute, and so
Speaker 1: we have proof because we have an auto trail, we
Speaker 1: have a log. So when we were imagining, we were
Speaker 1: imagining a business model that hasn't quite come up, but
Speaker 1: we have gotten a number of requests from insurance companies
Speaker 1: as well as companies to say we're interested in this idea.
Speaker 1: So how much has to be in that log? Does
Speaker 1: this have to be a whole video of your life?
Speaker 1: That is a great question, one that terrifies us. So
Speaker 1: it may be that you're logging locate geolocation, you're logging videos,
Speaker 1: you see people talking and who they're interacting with, and
Speaker 1: that might be good enough to prevent the mischief that
Speaker 1: would hijack the IP. Your whole life online, yes, stored securely,
Speaker 1: our clock down, protected in the cloud. It is, at
Speaker 1: least for a privacy scholar. There are so many reasons
Speaker 1: why we ought to have privacy that aren't about hiding things.
Speaker 1: It's about creating spaces and managing boundaries around ourselves and
Speaker 1: our intimates and our loved ones. So I worry that
Speaker 1: if we entirely unravel privacy A in the wrong hands
Speaker 1: is very dangerous. Right B It changes how we think
Speaker 1: about ourselves and humanity? Chapter nine, Section two thirty. So
Speaker 1: technofixes are complicated. What about passing laws to ban deep
Speaker 1: fakes or at least deep fakes that don't disclose their
Speaker 1: fake So the video and audio is speech in our
Speaker 1: First Amendment doctrine is very much a protective of free speech,
Speaker 1: and the Supreme Court has explained that lies just lies
Speaker 1: themselves without harm is protected speech. When lies cause certain
Speaker 1: kinds of harm, we can regulate it. Defamation of private people, threats, incitement, fraud,
Speaker 1: impersonation of government officials. What about lies concerning public figures
Speaker 1: like politicians? California and Texas, for instance, recently pass laws
Speaker 1: making it illegal to publish deep fakes of a candidate
Speaker 1: in the weeks leading up to an election. It's not
Speaker 1: clear yet whether the laws will pass constitutional muster. As
Speaker 1: you're saying in an American content, we are just not
Speaker 1: going to be able to law great fakes. Yeah, we
Speaker 1: can't have a flat van, and I don't think we should.
Speaker 1: It would fail on doctrinal grounds, but ultimately it would
Speaker 1: prevent the positive uses. Interestingly, in January twenty twenty, China,
Speaker 1: which has no First Amendment protecting free speech promulgated regulations
Speaker 1: banning deep fakes. The use of AI or virtuality now
Speaker 1: needs to be clearly marked in a prominent manner, and
Speaker 1: the failure to do so is considered a criminal offense.
Speaker 1: To explore other options for the US, I went to
Speaker 1: speak with a public policy expert. My name is Joan Donovan,
Speaker 1: and I work at Harvard Kennedy Shorenstein Center, where I
Speaker 1: lead a team of researchers looking at medium manipulation and
Speaker 1: disinformation campaigns. Joan is head of the Technology and Social
Speaker 1: Change Research Project, and her staff studies how social media
Speaker 1: gives rise to hoaxes and scams. Her team is particularly
Speaker 1: interested and precisely how misinformation spreads across the Internet. Ultimately,
Speaker 1: underneath all of this is the distribution mechanism, which is
Speaker 1: social media and platforms. And platforms have to rethink the
Speaker 1: openness of their design because that has now become a
Speaker 1: territory for information warfare. In early twenty twenty, Facebook announced
Speaker 1: a major policy change about synthesized content. Facebook preissued policies
Speaker 1: now on deep fake saying that if it is an
Speaker 1: AI generated video and it's misleading in some other contextual way,
Speaker 1: then they will remove it. Interestingly, Facebook ban the Moon
Speaker 1: Disaster Team's Nixon video even though it was made for
Speaker 1: educational purposes, but didn't remove the slowed down version of
Speaker 1: Nancy Pelosi, which was made to mislead the public. Why
Speaker 1: because the Pelosi video wasn't created with artificial elligience. For now,
Speaker 1: Facebook is choosing to target deep fakes, but not cheap fakes.
Speaker 1: One way to push platforms to take a stronger stance
Speaker 1: might be to remove some of the legal protections that
Speaker 1: they currently enjoy. Under Section two thirty of the Communication's
Speaker 1: Decency Act past in nineteen ninety six, platforms aren't legally
Speaker 1: liable for content posted by its users. The fact that
Speaker 1: platforms have no responsibility for the content they host has
Speaker 1: an upside. It's led to the massive diversity of online
Speaker 1: content we enjoyed today, but it also allows a dangerous
Speaker 1: escalation of fake news. Is it time to change section
Speaker 1: two thirty to create incentives for platforms to police false content?
Speaker 1: I ask the former head of a major platform, LinkedIn
Speaker 1: co founder Reid Hoffman. For example, let's take my view
Speaker 1: of what the response to the Christchurch shooting should be
Speaker 1: as to say, well, we want you to solve, not
Speaker 1: having terrorism, murderer or murderers displayed to people. So we're
Speaker 1: simply going to do a fine of ten thousand dollars
Speaker 1: per view. Two shootings occurred at mosques in Christchurch, New
Speaker 1: Zealand in March twenty nineteen. Graphic videos of the event
Speaker 1: were soon posted online. Five people saw it, that's fifty
Speaker 1: thousand dollars. But if he becomes a meme and a
Speaker 1: million people see it, that's ten billion dollars. Yes, right,
Speaker 1: So what's really trying to do is get you to say,
Speaker 1: let's make sure that the meme never happens. Okay, So
Speaker 1: that's a governance mechanism there. Yes, you find the channel
Speaker 1: the platform based on number of views would be a
Speaker 1: very general way to say. Now you guys have to solve.
Speaker 1: Now you solve, you figure it out. What about other solutions?
Speaker 1: If we are to make regulation, it should be about
Speaker 1: the amount of staff in proportion to the out of
Speaker 1: users so that they can get a handle on the content.
Speaker 1: But can they be fast enough. Maybe the viral spread
Speaker 1: should be slowed down enough to allow them to moderate.
Speaker 1: Let's put it this way. The stock market has certain
Speaker 1: governors built in when there's massive changes in a stock price.
Speaker 1: There are decelerators that kick in, breaks that kick in
Speaker 1: should the platforms have breaks that kick in before something
Speaker 1: can go fully viral. So in terms of deceleration, there
Speaker 1: are things that they do already that accelerate the process
Speaker 1: that they need to think differently about, especially when it
Speaker 1: comes to something turning into a trending topic. So there
Speaker 1: needs to be an intervening moment before things get to
Speaker 1: the homepage and get to trending, where there is a
Speaker 1: content review. So much to say here, but I want
Speaker 1: to think particularly about listeners who are in their twenties
Speaker 1: and thirties, are very tech savvy. They're going to be
Speaker 1: part of the solution here. What would you say to
Speaker 1: them about what they can do? I think it's important
Speaker 1: that younger people advocate for the Internet that they want.
Speaker 1: We have to fight for it, We have to ask
Speaker 1: for different things, and that kind of agitation can come
Speaker 1: in the form of posting on the platform, writing letters,
Speaker 1: joining groups like Fight for the Future, and trying to
Speaker 1: work on getting platforms to do better and to advocate
Speaker 1: for the kind of content that you want to see
Speaker 1: more of. The important thing is that our society is
Speaker 1: shaped by these platforms and so we're not going to
Speaker 1: do away with them, but we don't have to make
Speaker 1: do with them either. Conclusion, choose your planet. So there
Speaker 1: you have it. Stewards of the Brave New Planet. Synthetic
Speaker 1: media or deep fakes. People have been manipulating content for
Speaker 1: more than a hundred years, but recent advances in AI
Speaker 1: have taken it to a whole new level of verisimilitude.
Speaker 1: The technology could transform movies and television, favored actors from
Speaker 1: years past starring in new narratives, along with actors who
Speaker 1: never existed, patients regaining the ability to speak in their
Speaker 1: own voices, personalized stories created on demand for any child
Speaker 1: around the globe, matching their interests, written in their dialect,
Speaker 1: representing their communities. But there's also great potential for harm,
Speaker 1: the ability to cast anyone in a pornographic video, weaponized
Speaker 1: media dropping days before an election, or provoking international conflicts.
Speaker 1: Are we going to be able to tell fact from fiction?
Speaker 1: Will truth survive? And what does it mean for our democracy? Better?
Speaker 1: Fake detection may help, but it'll be hard for it
Speaker 1: to keep up, and logging our lives in blockchain to
Speaker 1: protect against misrepresentation doesn't sound like an attractive idea. Outright
Speaker 1: bands on deep fakes are being tried in some countries,
Speaker 1: but they're tricky in the US given our constitutional protections
Speaker 1: for free speech. Maybe the best solution is to put
Speaker 1: the liability on platforms like Facebook and YouTube. If we
Speaker 1: can joan Donovan's right to get the future you want,
Speaker 1: you're going to have to fight for it. You don't
Speaker 1: have to be an expert, and you don't have to
Speaker 1: do it alone. When enough people get engaged, we make
Speaker 1: wise choices. Deep fakes are a problem that everyone can
Speaker 1: engage with. Brainstorm with your friends about what should be done.
Speaker 1: Use social media. Tweet at your elected representatives to ask
Speaker 1: if they're working on laws, like in California and Texas.
Speaker 1: And if you work for a tech company, ask yourself
Speaker 1: and your colleagues if you're doing enough. You can find
Speaker 1: lots of resources and ideas at our website Brave New
Speaker 1: Planet dot org. It's time to choose our planet. The
Speaker 1: future is up to us. Brave New Planet is a
Speaker 1: coproduction of the Broad Institute of MT and Harvard Pushkin
Speaker 1: Industries in the Boston Globe, with support from the Alfred P.
Speaker 1: Sloan Foundation. Our show is produced by Rebecca Lee Douglas
Speaker 1: with Mary Doo theme song composed by Ned Porter, mastering
Speaker 1: and sound designed by James Garver, fact checking by as
Speaker 1: If Fridman and a Stitt and Enchant. Special thanks to
Speaker 1: Christine Heenan and Rachel Roberts at Clarendon Communications, to Lee McGuire,
Speaker 1: Kristen Zarelli and Justine Levin Allerhans at the Broad, to
Speaker 1: mil Lobell and Heather Faine at Pushkin, and to Eliah
Speaker 1: Edie Brode who made the Broad Institute possible. This is
Speaker 1: brave new planet. I'm Eric Lander.